Ingesting External Data into Azure AI Search with Azure Data Factory

Organizations store critical information across multiple cloud platforms, on-premises servers, and third-party APIs, making it challenging to build a unified search experience. Connecting these disparate data silos to your search index is essential for modern, data-driven applications. This text-only course guides you through the process of building robust data pipelines that ingest external data sources into Azure AI Search. You will learn how to design, configure, and secure your ingestion workflows to keep your search index accurate and up to date. What you'll learn: Understand the foundational architecture of Azure AI Search and Azure Data Factory; Configure linked services and datasets to securely connect to data sources outside the Azure platform; Build data pipelines that transform and map external data schemas directly to search index fields; Apply incremental loading strategies to ensure your search index updates efficiently without full rebuilds; Implement modern security practices, including managed identities and secure credential storage, for your pipelines; Practice troubleshooting pipeline runs and monitoring data ingestion performance through written scenarios. The course starts with essential terminology and structural concepts of search indexing and cloud data orchestration. You will then progress through step-by-step written guides and configuration examples to build end-to-end ingestion pipelines for external data. Designed for beginners, cloud enthusiasts, and database administrators new to search indexing, with no prior pipeline development experience required. Start reading today to unlock the full potential of your external data within Azure AI Search.